{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T16:49:14Z","timestamp":1755794954066,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":67,"publisher":"ACM","funder":[{"name":"The Joint Foundation of the Ministry of Education for Innovation team","award":["8091B042235"],"award-info":[{"award-number":["8091B042235"]}]},{"name":"Beijing Natural Science Foundation","award":["4244096"],"award-info":[{"award-number":["4244096"]}]},{"name":"The National Natural Science Foundation of China","award":["62406019, 62436001, 62176020"],"award-info":[{"award-number":["62406019, 62436001, 62176020"]}]},{"name":"The State Key Laboratory of Rail Traffic Control and Safety","award":["RCS2023K006"],"award-info":[{"award-number":["RCS2023K006"]}]},{"name":"the Talent Fund of Beijing Jiaotong University","award":["2024XKRC075"],"award-info":[{"award-number":["2024XKRC075"]}]},{"name":"The National Key Research and Development Program of China","award":["2024YFE0202900"],"award-info":[{"award-number":["2024YFE0202900"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8,3]]},"DOI":"10.1145\/3711896.3737020","type":"proceedings-article","created":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T21:03:27Z","timestamp":1754255007000},"page":"1799-1810","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning OOD Robust Neural Operator with Risk-Averse Stochastic Optimization"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7914-6867","authenticated-orcid":false,"given":"Huafeng","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China and Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5541-8003","authenticated-orcid":false,"given":"Yiran","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China and Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6790-7726","authenticated-orcid":false,"given":"Jingyue","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China and Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7578-3407","authenticated-orcid":false,"given":"Liping","family":"Jing","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China, Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University, Beijing, China, and State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0902-3121","authenticated-orcid":false,"given":"Jian","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China and Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"ICLR 2020 Workshop on Integration of Deep Neural Models and Differential Equations.","author":"Anandkumar Anima","year":"2020","unstructured":"Anima Anandkumar, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Nikola Kovachki, Zongyi Li, Burigede Liu, and Andrew Stuart. 2020. Neural operator: Graph kernel network for partial differential equations. In ICLR 2020 Workshop on Integration of Deep Neural Models and Differential Equations."},{"key":"e_1_3_2_1_2_1","series-title":"SIAM journal on computing","volume-title":"The nonstochastic multiarmed bandit problem","author":"Auer Peter","year":"2002","unstructured":"Peter Auer, Nicolo Cesa-Bianchi, Yoav Freund, and Robert E Schapire. 2002. The nonstochastic multiarmed bandit problem. SIAM journal on computing, Vol. 32, 1 (2002), 48-77."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0493(1915)43<163:SRROTM>2.0.CO;2"},{"key":"e_1_3_2_1_4_1","volume-title":"Robust solutions of uncertain linear programs. Operations research letters","author":"Ben-Tal Aharon","year":"1999","unstructured":"Aharon Ben-Tal and Arkadi Nemirovski. 1999. Robust solutions of uncertain linear programs. Operations research letters, Vol. 25, 1 (1999), 1-13."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Jose Antonio Lara Benitez Takashi Furuya Florian Faucher Anastasis Kratsios Xavier Tricoche and Maarten V de Hoop. 2024. Out-of-distributional risk bounds for neural operators with applications to the Helmholtz equation. J. Comput. Phys.(2024) 113168.","DOI":"10.1016\/j.jcp.2024.113168"},{"key":"e_1_3_2_1_6_1","volume-title":"Jinye Zhao, and Tongxin Zheng.","author":"Bertsimas Dimitris","year":"2012","unstructured":"Dimitris Bertsimas, Eugene Litvinov, Xu Andy Sun, Jinye Zhao, and Tongxin Zheng. 2012. Adaptive robust optimization for the security constrained unit commitment problem. IEEE transactions on power systems, Vol. 28, 1 (2012), 52-63."},{"key":"e_1_3_2_1_7_1","volume-title":"The price of robustness. Operations research","author":"Bertsimas Dimitris","year":"2004","unstructured":"Dimitris Bertsimas and Melvyn Sim. 2004. The price of robustness. Operations research, Vol. 52, 1 (2004), 35-53."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.126.098302"},{"key":"e_1_3_2_1_9_1","volume-title":"Choose a transformer: Fourier or galerkin. Advances in neural information processing systems","author":"Cao Shuhao","year":"2021","unstructured":"Shuhao Cao. 2021. Choose a transformer: Fourier or galerkin. Advances in neural information processing systems, Vol. 34 (2021), 24924-24940."},{"key":"e_1_3_2_1_10_1","unstructured":"Quasi-Monte Carlo. 2001. Monte Carlo methods in financial engineering. (2001)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2020.109942"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013321"},{"key":"e_1_3_2_1_13_1","first-page":"1","article-title":"Risk-constrained reinforcement learning with percentile risk criteria","volume":"18","author":"Chow Yinlam","year":"2018","unstructured":"Yinlam Chow, Mohammad Ghavamzadeh, Lucas Janson, and Marco Pavone. 2018. Risk-constrained reinforcement learning with percentile risk criteria. Journal of Machine Learning Research, Vol. 18, 167 (2018), 1-51.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_14_1","first-page":"1036","article-title":"Adaptive sampling for stochastic risk-averse learning","volume":"33","author":"Curi Sebastian","year":"2020","unstructured":"Sebastian Curi, Kfir Y Levy, Stefanie Jegelka, and Andreas Krause. 2020. Adaptive sampling for stochastic risk-averse learning. Advances in Neural Information Processing Systems, Vol. 33 (2020), 1036-1047.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_15_1","unstructured":"Shaan Desai Marios Mattheakis Hayden Joy Pavlos Protopapas and Stephen Roberts. 2021. One-shot transfer learning of physics-informed neural networks. arXiv preprint arXiv:2110.11286(2021)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-43465-6_1"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/763"},{"key":"e_1_3_2_1_18_1","volume-title":"Learning with average top-k loss. Advances in neural information processing systems","author":"Fan Yanbo","year":"2017","unstructured":"Yanbo Fan, Siwei Lyu, Yiming Ying, and Baogang Hu. 2017. Learning with average top-k loss. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_19_1","volume-title":"Recent advances in robust optimization: An overview. European journal of operational research","author":"Gabrel Virginie","year":"2014","unstructured":"Virginie Gabrel, C\u00e9cile Murat, and Aur\u00e9lie Thiele. 2014. Recent advances in robust optimization: An overview. European journal of operational research, Vol. 235, 3 (2014), 471-483."},{"key":"e_1_3_2_1_20_1","first-page":"22209","article-title":"Two steps to risk sensitivity","volume":"34","author":"Gagne Christopher","year":"2021","unstructured":"Christopher Gagne and Peter Dayan. 2021. Two steps to risk sensitivity. Advances in Neural Information Processing Systems, Vol. 34 (2021), 22209-22220.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Jun-ya Gotoh and Akiko Takeda. 2016. CVaR minimizations in support vector machines. Financial Signal Processing and Machine Learning(2016) 233-265.","DOI":"10.1002\/9781118745540.ch10"},{"volume-title":"Monte carlo methods","author":"Hammersley John","key":"e_1_3_2_1_22_1","unstructured":"John Hammersley. 2013. Monte carlo methods. Springer Science & Business Media."},{"key":"e_1_3_2_1_23_1","unstructured":"Zhongkai Hao Songming Liu Yichi Zhang Chengyang Ying Yao Feng Hang Su and Jun Zhu. 2023. Physics-informed machine learning: A survey on problems methods and applications. arXiv preprint arXiv:2211.08064(2023)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42254-021-00314-5"},{"volume-title":"Monte Carlo methods and models in finance and insurance","author":"Korn Ralf","key":"e_1_3_2_1_25_1","unstructured":"Ralf Korn, Elke Korn, and Gerald Kroisandt. 2010. Monte Carlo methods and models in finance and insurance. CRC press."},{"key":"e_1_3_2_1_26_1","volume-title":"Characterizing possible failure modes in physics-informed neural networks. Advances in neural information processing systems","author":"Krishnapriyan Aditi","year":"2021","unstructured":"Aditi Krishnapriyan, Amir Gholami, Shandian Zhe, Robert Kirby, and Michael W Mahoney. 2021. Characterizing possible failure modes in physics-informed neural networks. Advances in neural information processing systems, Vol. 34 (2021), 26548-26560."},{"key":"e_1_3_2_1_27_1","volume-title":"International conference on machine learning. PMLR, 5815-5826","author":"Krueger David","year":"2021","unstructured":"David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, and Aaron Courville. 2021. Out-of-distribution generalization via risk extrapolation (rex). In International conference on machine learning. PMLR, 5815-5826."},{"key":"e_1_3_2_1_28_1","first-page":"6755","article-title":"Multipole graph neural operator for parametric partial differential equations","volume":"33","author":"Li Zongyi","year":"2020","unstructured":"Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Andrew Stuart, Kaushik Bhattacharya, and Anima Anandkumar. 2020. Multipole graph neural operator for parametric partial differential equations. Advances in Neural Information Processing Systems, Vol. 33 (2020), 6755-6766.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_29_1","volume-title":"Fourier Neural Operator for Parametric Partial Differential Equations. In International Conference on Learning Representations.","author":"Li Zongyi","year":"2021","unstructured":"Zongyi Li, Nikola Borislavov Kovachki, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar, et al., 2021. Fourier Neural Operator for Parametric Partial Differential Equations. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_30_1","first-page":"1","article-title":"Physics-informed neural operator for learning partial differential equations","volume":"1","author":"Li Zongyi","year":"2024","unstructured":"Zongyi Li, Hongkai Zheng, Nikola Kovachki, David Jin, Haoxuan Chen, Burigede Liu, Kamyar Azizzadenesheli, and Anima Anandkumar. 2024. Physics-informed neural operator for learning partial differential equations. ACM\/JMS Journal of Data Science, Vol. 1, 3 (2024), 1-27.","journal-title":"ACM\/JMS Journal of Data Science"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671676"},{"key":"e_1_3_2_1_32_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 6822-6838","author":"Liu Ning","year":"2023","unstructured":"Ning Liu, Yue Yu, Huaiqian You, and Neeraj Tatikola. 2023. Ino: Invariant neural operators for learning complex physical systems with momentum conservation. In International Conference on Artificial Intelligence and Statistics. PMLR, 6822-6838."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07294-2"},{"key":"e_1_3_2_1_34_1","volume-title":"Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nature machine intelligence","author":"Lu Lu","year":"2021","unstructured":"Lu Lu, Pengzhan Jin, Guofei Pang, Zhongqiang Zhang, and George Em Karniadakis. 2021. Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nature machine intelligence, Vol. 3, 3 (2021), 218-229."},{"key":"e_1_3_2_1_35_1","unstructured":"Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras and Adrian Vladu. 2017. Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083(2017)."},{"key":"e_1_3_2_1_36_1","volume-title":"Liam Holden Parker, Ruben Ohana, Miles Cranmer, Alberto Bietti, Michael Eickenberg, Siavash Golkar, Geraud Krawezik, Francois Lanusse, et al.","author":"McCabe Michael","year":"2024","unstructured":"Michael McCabe, Bruno R\u00e9galdo-Saint Blancard, Liam Holden Parker, Ruben Ohana, Miles Cranmer, Alberto Bietti, Michael Eickenberg, Siavash Golkar, Geraud Krawezik, Francois Lanusse, et al., 2024. Multiple physics pretraining for spatiotemporal surrogate models. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_37_1","volume-title":"The Twelfth International Conference on Learning Representations.","author":"Mouli S Chandra","year":"2024","unstructured":"S Chandra Mouli, Muhammad Alam, and Bruno Ribeiro. 2024a. MetaPhysiCa: Improving OOD robustness in physics-informed machine learning. In The Twelfth International Conference on Learning Representations."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.5555\/3692070.3693550"},{"key":"e_1_3_2_1_39_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"N\u00e9giar Geoffrey","year":"2023","unstructured":"Geoffrey N\u00e9giar, Michael W Mahoney, and Aditi Krishnapriyan. 2023. Learning differentiable solvers for systems with hard constraints. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_40_1","series-title":"SIAM Journal on optimization","volume-title":"Robust stochastic approximation approach to stochastic programming","author":"Nemirovski Arkadi","year":"2009","unstructured":"Arkadi Nemirovski, Anatoli Juditsky, Guanghui Lan, and Alexander Shapiro. 2009. Robust stochastic approximation approach to stochastic programming. SIAM Journal on optimization, Vol. 19, 4 (2009), 1574-1609."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0020-0190(02)00370-8"},{"key":"e_1_3_2_1_42_1","unstructured":"Francesco Orabona. 2019. A modern introduction to online learning. arXiv preprint arXiv:1912.13213(2019)."},{"key":"e_1_3_2_1_43_1","volume-title":"International conference on machine learning. PMLR, 2817-2826","author":"Pinto Lerrel","year":"2017","unstructured":"Lerrel Pinto, James Davidson, Rahul Sukthankar, and Abhinav Gupta. 2017. Robust adversarial reinforcement learning. In International conference on machine learning. PMLR, 2817-2826."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2022.111121"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbankfin.2007.12.025"},{"key":"e_1_3_2_1_46_1","unstructured":"Hamed Rahimian and Sanjay Mehrotra. 2019. Distributionally robust optimization: A review. arXiv preprint arXiv:1908.05659(2019)."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2018.10.045"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)EM.1943-7889.0001947"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.21314\/JOR.2000.038"},{"key":"e_1_3_2_1_50_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Saad Nadim","year":"2024","unstructured":"Nadim Saad, Gaurav Gupta, Shima Alizadeh, and Danielle C Maddix. 2024. Guiding continuous operator learning through Physics-based boundary constraints. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_51_1","volume-title":"Tatsunori B Hashimoto, and Percy Liang.","author":"Sagawa Shiori","year":"2019","unstructured":"Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang. 2019. Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. arXiv preprint arXiv:1911.08731(2019)."},{"key":"e_1_3_2_1_52_1","volume-title":"Risk-aversion in multi-armed bandits. Advances in neural information processing systems","author":"Sani Amir","year":"2012","unstructured":"Amir Sani, Alessandro Lazaric, and R\u00e9mi Munos. 2012. Risk-aversion in multi-armed bandits. Advances in neural information processing systems, Vol. 25 (2012)."},{"key":"e_1_3_2_1_53_1","volume-title":"New support vector algorithms. Neural computation","author":"Sch\u00f6lkopf Bernhard","year":"2000","unstructured":"Bernhard Sch\u00f6lkopf, Alex J Smola, Robert C Williamson, and Peter L Bartlett. 2000. New support vector algorithms. Neural computation, Vol. 12, 5 (2000), 1207-1245."},{"key":"e_1_3_2_1_54_1","first-page":"70581","article-title":"Operator learning with neural fields: Tackling pdes on general geometries","volume":"36","author":"Serrano Louis","year":"2023","unstructured":"Louis Serrano, Lise Le Boudec, Armand Kassa\u00ef Koupa\u00ef, Thomas X Wang, Yuan Yin, Jean-No\u00ebl Vittaut, and Patrick Gallinari. 2023. Operator learning with neural fields: Tackling pdes on general geometries. Advances in Neural Information Processing Systems, Vol. 36 (2023), 70581-70611.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_55_1","volume-title":"International Conference on Machine Learning. PMLR, 793-801","author":"Shalev-Shwartz Shai","year":"2016","unstructured":"Shai Shalev-Shwartz and Yonatan Wexler. 2016. Minimizing the maximal loss: How and why. In International Conference on Machine Learning. PMLR, 793-801."},{"volume-title":"Density estimation for statistics and data analysis","author":"Silverman Bernard W","key":"e_1_3_2_1_56_1","unstructured":"Bernard W Silverman. 2018. Density estimation for statistics and data analysis. Routledge."},{"key":"e_1_3_2_1_57_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Subramanian Shashank","year":"2024","unstructured":"Shashank Subramanian, Peter Harrington, Kurt Keutzer, Wahid Bhimji, Dmitriy Morozov, Michael W Mahoney, and Amir Gholami. 2024. Towards foundation models for scientific machine learning: Characterizing scaling and transfer behavior. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_1_58_1","volume-title":"Principles of risk minimization for learning theory. Advances in neural information processing systems","author":"Vapnik Vladimir","year":"1991","unstructured":"Vladimir Vapnik. 1991. Principles of risk minimization for learning theory. Advances in neural information processing systems, Vol. 4 (1991)."},{"key":"e_1_3_2_1_59_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Wang Qi","year":"2024","unstructured":"Qi Wang, Yiqin Lv, Zheng Xie, Jincai Huang, et al., 2024. A simple yet effective strategy to robustify the meta learning paradigm. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1137\/20M1318043"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2021.110768"},{"key":"e_1_3_2_1_62_1","volume-title":"Integrating physics-based modeling with machine learning: A survey. arXiv preprint arXiv:2003.04919","author":"Willard Jared","year":"2020","unstructured":"Jared Willard, Xiaowei Jia, Shaoming Xu, Michael Steinbach, and Vipin Kumar. 2020. Integrating physics-based modeling with machine learning: A survey. arXiv preprint arXiv:2003.04919, Vol. 1, 1 (2020), 1-34."},{"key":"e_1_3_2_1_63_1","volume-title":"International conference on machine learning. PMLR, 6786-6797","author":"Williamson Robert","year":"2019","unstructured":"Robert Williamson and Aditya Menon. 2019. Fairness risk measures. In International conference on machine learning. PMLR, 6786-6797."},{"key":"e_1_3_2_1_64_1","volume-title":"Prometheus: Out-of-distribution Fluid Dynamics Modeling with Disentangled Graph ODE. In Forty-first International Conference on Machine Learning.","author":"Wu Hao","year":"2024","unstructured":"Hao Wu, Huiyuan Wang, Kun Wang, Weiyan Wang, Yangyu Tao, Chong Chen, Xian-Sheng Hua, Xiao Luo, et al., 2024. Prometheus: Out-of-distribution Fluid Dynamics Modeling with Disentangled Graph ODE. In Forty-first International Conference on Machine Learning."},{"key":"e_1_3_2_1_65_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Yin Yuan","year":"2023","unstructured":"Yuan Yin, Matthieu Kirchmeyer, Jean-Yves Franceschi, Alain Rakotomamonjy, and Patrick Gallinari. 2023. Continuous PDE dynamics forecasting with implicit neural representations. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/ac3ae5"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2023.116280"}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Toronto ON Canada","acronym":"KDD '25"},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3711896.3737020","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,16]],"date-time":"2025-08-16T14:38:23Z","timestamp":1755355103000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711896.3737020"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":67,"alternative-id":["10.1145\/3711896.3737020","10.1145\/3711896"],"URL":"https:\/\/doi.org\/10.1145\/3711896.3737020","relation":{},"subject":[],"published":{"date-parts":[[2025,8,3]]},"assertion":[{"value":"2025-08-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}